MCnet is a European Union funded Marie Curie Initial Training Network dedicated to developing and supporting general-purpose Monte Carlo event generators throughout the LHC era and beyond, and providing training of a wide selection of its user base, particularly through funded short-term 'residencies' and Annual Schools.
MCnet is funded by the European Union's Horizon 2020 research and training innovation programme as the Marie Skłodowska Curie Innovative Training Network MCnetITN3 (grant agreement no. 722104).
Monte Carlo event generators are central to high energy particle physics. They are used by almost all experimental collaborations to plan their experiments and analyze their data, and by theorists to simulate the complex final states of the fundamental interactions that may signal new physics. The network incorporates all the authors of current general purpose event generators, and has the main purposes of:
- training a large section of our user base, using annual schools on the physics and techniques of event generators and short-term residencies of Early Stage Researchers as a conduit for transfer of knowledge to the wider community;
- training the next generation of event generator authors through a significant number of dedicated studentships in our research groups;
- providing broader training in transferable skills through our research, through dedicated training in entrepreneurship, employability and outreach and through secondments to private sector partners.
These training objectives are being achieved both through dedicated activities and through our outreach and research activities:
- enhancing the visibility of particle physics in the wider community by specific outreach projects using event generators to visualize current particle physics research;
- developing and supporting the new generation of event generators intended for use throughout the LHC data analysis era and beyond;
- playing a central role in the analysis of LHC data and the discovery of new particles and interactions there; and
- extracting the maximum potential from existing data to constrain the modeling of the data from the LHC and future experiments.